This article is 'Highly accessed'  (total accesses to this article since publication: 1197) relative to age  in  BioMed Central: Increasing risk behaviour can outweigh the benefits of antiretroviral drug treatment on the HIV incidence among men-having-sex-with-men in Amsterdam Shan Mei, Rick Quax, David VAN de Vijver, Yifan Zhu and P.m.a. Sloot BMC Infectious Diseases, 11:118   (11 May 2011)

As of August 27, the most downloaded/accessed paper in BMC Systems Biology is: D. van Dijk et al.: Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks, BMC Systems Biology, vol. 4, nr 1 pp. 96+17. 2010.

This paper received the ICCS best paper award 2010:
N. Zarrabi et al.: Modeling HIV-1 intracellular replication, Procedia Computer Science vol. 1, nr 1 pp. 555-564. Elsevier B.V., Amsterdam, May 2010.


DyA Package




DIP (The DynaNets Integration Platform)


DyA Package

Recently many important programming tools have been developed to facilitate network analysis, but few of these programming tools take into account the dynamic behavior of the network. The R package iGraph is a useful collection to do analysis of a graph in the statical sense.
We are developing a new R package, Dya, which allows the user to perform analysis on a series of graphs, thus focusing on the dynamic trends of the networks.  We use three approaches of these dynamic trends.

  1. Global level analysis,
  2. Local level analysis, and
  3. Analysis of network clusters

In the global level approach (1) we are trying to find the answers to such questions, as "How does the average path length of the network change?". So we are focusing on the whole graph. In the local level approach (2), we are looking at the local nodes of the networks, or some selected local nodes. As a graph may have many vertices, often we are focusing on just some 'relevant' local nodes. For these importance measures we currently use the iGraph centrality calculation functions (although other local measures are possible). Also we do stability tests of the neighborhood of relevant nodes. In the cluster level approach (3) we are trying to visualize the evolution of the network clusters. We are trying to find out if a cluster at a given time has developed from another cluster in the previous slice of time.
The functions of the package Dya were prepared to visualize the dynamic network trends. To help expressive visualization the functions make diagrams. In the package we also provide functions for building dynamic networks from CSV files.

The DyA  Package and documentation can be downloaded here.


Dyneta is a program that generates networks (big, usually sparse graphs), runs an event on them and collects some statistics.
Dyneta is highly configurable - that means that it includes a wide range of network models and several events and statistics as well. The network models and events may have parameters as well. Also, Dyneta has a plugin-based architecture and thus it can be extended easily.

The Dyneta  Package and documentation can be downloaded here.

SexNet (in NetLogo 4.1)

SexNet is a flexible agent based (individual based) model for the generative study of human sexual contact networks and infections (such as HIV) spreading on them. This is the first general model we know of that offers a systematic study of various contact regimes (ie. random, preferential, and assortative), as well as the use of different temporal sampling (aggregation) windows for the study of dynamic behavior. Despite the many different combinations it offers, this is a baseline model only than should be extended in a number of relevant dimensions.

Download here.


SEECN (pronounce: "see-sn" like in "season")

The simulation of dynamical processes unfolding upon a dynamical network is often a computationally daunting task as it may require the simulation of phenomena taking place on vastly different time scales. In addition, the complex networks that arise from such models will be so large that performance becomes an issue. Indeed multiscale, multiphysics systems are too complex for accurate analytical treatments, yet such systems arise everywhere from modeling the immune system and protein interaction to epidemic spread in a human population.

To address these problems we have developed a new tool, the Simulator for Efficient Evolution on Complex Networks (SEECN [23]), an expressive simulator of complex
systems. Emphasis was given to the optimization and fine-tuning of the simulator which aims at showing the feasibility of a network science approach to problems which have been traditionally considered too demanding for accurate numerical simulation. In SEECN, a complex network represents the system where the nodes and edges have specified properties which dictate the dynamics of the network over time. It features the capability of integrating the information from different time scales while confirming the computational feasibility of agent-based modeling combined with complex networks. As a case study to validate SEECN, we developed a detailed model of HIV spread among men who have sex with men and serves to show the simulator's expressiveness and to evaluate its performance. The investigation of the spreading of HIV and its drug resistance represents an excellent stress test for SEECN since requires a holistic approach of various dynamics at multiple spatiotemporal scales.

Download here.


DynaNets Integrated Platform (DIP)

An important goal is to provide a software platform that integrates models developed in DynaNets together with ongoing assistance to scientists using the platform in the form of maintenance, customizations, extensions, fixes & documentation. The DynaNets Integrated Platform (DIP) allows individual researchers to carry out computational experiments on dynamic changing complex networks from end to end in a single computational environment. It further promotes reuse of software components for the construction of scientific computational workflows and allows DynaNets researchers to collaborate by sharing such components and workflows. In this way the DIP supports the complete spectrum of instrumental abilities for conducting computational studies throughout the entire life-cycle of the modelling process.

Download here.

Real networks are dynamic

EU Grant Agreement

Number: 233847

BONUS Grant Number:


FET Open

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